基本信息
- 标题: "DiffWave: A Versatile Diffusion Model for Audio Synthesis"
- 作者:
- 01 Zhifeng Kong,
- 02 Wei Ping,
- 03 Jiaji Huang,
- 04 Kexin Zhao,
- 05 Bryan Catanzaro
- 链接:
- ArXiv
- Publication
- [Github]
- Demo
- 文件:
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In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in different waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster. In particular, it significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task in terms of audio quality and sample diversity from various automatic and human evaluations. |